, research has suggested a direct correlation between environmental pollution and contagion dynamics (i.e., environment-to-human pollution), thereby indicating that mechanisms other than human-to-human transmission can explain COVID-19 diffusion. However, these studies did not consider that complex outcomes, such as a pandemic's diffusion patterns, are typically caused by a multiplicity of environmental, economic and social factors. While disciplinary specialties increase scholars' attitudes of concentrating on specific factors, neglecting this multiplicity during a pandemic crisis can lead to misleading conclusions. This communication aims to focus on certain limitations of current research about environmental-to-human COVID-19 transmission and shows the benefit of an interdisciplinary, multi-dimensional approach to understand the geographical diversity of contagion diffusion patterns.

To increase transparency in science, some scholarly journals are publishing peer review reports. But it is unclear how this practice affects the peer review process. Here, we examine the effect of publishing peer review reports on referee behavior in five scholarly journals involved in a pilot study at Elsevier. By considering 9,220 submissions and 18,525 reviews from 2010 to 2017, we measured changes both before and during the pilot and found that publishing reports did not significantly compromise referees’ willingness to review, recommendations, or turn-around times. Younger and non-academic scholars were more willing to accept to review and provided more positive and objective recommendations. Male referees tended to write more constructive reports during the pilot. Only 8.1% of referees agreed to reveal their identity in the published report. These findings suggest that open peer review does not compromise the process, at least when referees are able to protect their anonymity.

The COVID-pandemic is causing a dramatic loss of lives worldwide, challenging the sustainability of our health care systems, threatening economic meltdown, and putting pressure on the mental health of individuals (due to social distancing and lock-down measures). The pandemic is also posing severe challenges to the scientific community, with scholars under pressure to respond to policymakers' demands for advice despite the absence of adequate, trusted data. Understanding the pandemic requires fine-grained data representing specific local conditions and the social reactions of individuals. While experts have built simulation models to estimate disease trajectories that may be enough to guide decision-makers to formulate policy measures to limit the epidemic, they do not cover the full behavioural and social complexity of societies under pandemic crisis. Modelling that has such a large potential impact upon people's lives is a great responsibility. This paper calls on the scientific community to improve the transparency, access, and rigour of their models. It also calls on stakeholders to improve the rapidity with which data from trusted sources are released to the community (in a fully responsible manner). Responding to the pandemic is a stress test of our collaborative capacity and the social/economic value of research.

During the early months of the COVID-19 pandemic, there was an unusually high submission rate of scholarly articles. Given that most academics were forced to work from home, the competing demands for familial duties may have penalized the scientific productivity of women. To test this hypothesis, we looked at submitted manuscripts and peer review activities for all Elsevier journals between February and May 2018-2020, including data on over 5 million authors and referees. Results showed that during the first wave of the pandemic, women submitted proportionally fewer manuscripts than men. This deficit was especially pronounced among more junior cohorts of women academics. The rate of the peer-review invitation acceptance showed a less pronounced gender pattern with women taking on a greater service responsibility for journals, except for health & medicine, the field where the impact of COVID-19 research has been more prominent. Our findings suggest that the first wave of the pandemic has created potentially cumulative advantages for men.

How one builds, checks, validates and interprets a model depends on its 'purpose'. This is true even if the same model code is used for di erent purposes. This means that a model built for one purpose but then used for another needs to be re-justified for the new purpose and this will probably mean it also has to be rechecked, re-validated and maybe even rebuilt in a di erent way. Here we review some of the di erent purposes for a simulation model of complex social phenomena, focusing on seven in particular: prediction, explanation, description, theoretical exploration, illustration, analogy, and social interaction. The paper looks at some of the implications in terms of the ways in which the intended purpose might fail. This analysis motivates some of the ways in which these 'dangers' might be avoided or mitigated. It also looks at the ways that a confusion of modelling purposes can fatally weaken modelling projects, whilst giving a false sense of their quality. These distinctions clarify some previous debates as to the best modelling strategy (e.g. KISS and KIDS). The paper ends with a plea for modellers to be clear concerning which purpose they are justifying their model against.

Most of the intriguing social phenomena of our time, such as international terrorism, social inequality, and urban ethnic segregation, are consequences of complex forms of agent interaction that are difficult to observe methodically and experimentally. This book looks at a new research stream that makes use of advanced computer simulation modelling techniques to spotlight agent interaction that allows us to explain the emergence of social patterns. It presents a method to pursue analytical sociology investigations that look at relevant social mechanisms in various empirical situations, such as markets, urban cities, and organisations.\ud This book: \ud •Provides a comprehensive introduction to epistemological, theoretical and methodological features of agent-based modelling in sociology through various discussions and examples.\ud •Presents the pros and cons of using agent-based models in sociology. \ud •Explores agent-based models in combining quantitative and qualitative aspects, and micro- and macro levels of analysis.\ud •Looks at how to pose an agent-based research question, identifying the model building blocks, and how to validate simulation results.\ud •Features examples of agent-based models that look at crucial sociology issues.\ud •Supported by an accompanying website featuring data sets and code for the models included in the book.\ud \ud Agent-Based Computational Sociology is written in a common sociological language and features examples of models that look at all the traditional explanatory challenges of sociology. Researchers and graduate students involved in the field of agent-based modelling and computer simulation in areas such as social sciences, cognitive sciences and computer sciences will benefit from this book

a b s t r a c tThe importance of reputation in human societies is highlighted both by theoretical models and empirical studies. In this paper, we have extended the scope of previous experimental studies based on trust games by creating treatments where players can rate their opponents' behavior and know their past ratings. Our results showed that being rated by other players and letting this rating be known are factors that increase cooperation levels even when rational reputational investment motives are ruled out. More generally, subjects tended to respond to reputational opportunities even when this was neither rational nor explainable by reciprocity.

Abstract. Although peer review is crucial for innovation and experimental discoveries in science, it is poorly understood in scientific terms. Discovering its true dynamics and exploring adjustments which improve the commitment of everyone involved could benefit scientific development for all disciplines and consequently increase innovation in the economy and the society. We have reported the results of an innovative experiment developed to model peer review. We demonstrate that offering material rewards to reviewers tends to decrease the quality and efficiency of the reviewing process. Our findings help to discuss the viability of different options of incentive provision, supporting the idea that journal editors and responsible of research funding agencies should be extremely careful in offering material incentives on reviewing, since these might undermine moral motives which guide reviewers' behavior.